Unpacking Pecking Orders to Get the Gist of Web Gab

June 5, 2006

A USC Information Sciences Institute system pulls answers
from online conversations by identifying the alpha chatterers.

The system, presented at a conference on human
language technology on June 6, was developed to analyze
technical
conversations in which an objectively correct answer exists.
But the method for statistically characterizing response by
the group to individuals is generalizable.

Online communities are now firmly established in domains
ranging from high school gossip to professional open-source
software design discussions, generating huge repositories of
records of human knowledge processing, pre-converted to
digital form.

Such sites provide raw material for a new method that may,
among other things, enable Internet chat room users to get
a statistical measurement of their influence in their
room.

This research is one of the first
quantitative studies in the field of natural language
processing that takes account of the fact that chat
conversations are structured interactions among a large
number of people.

In the long term, research in this
area
will lead to the development of systems that can
automatically produce reports and summaries of meetings,
researchers hope.

It's easy to simply harvest factoids from text, said Hovy
(left),
who holds an appointment as research associate professor in
the USC Viterbi School of Engineering department of
computer science in addition to his post as deputy director of
the ISI Intelligent Systems Division and director of the ISI
Natural Language Group.

But the fact that human conversation has an inherent
structure, including temporal ordering, references to
previous statements, labeled sourcing and other clues opens
the door to much deeper machine-generated
understanding.

To make use of the structure, the team used a graph-based
algorithm called HITS (Hypertext Induced Topic Selection)
originally used by Cornell computer scientist Joel Kleinberg
to rank and classify web pages by their connections to each
other.

In the study, connections between conversation participants
replace the web links for the HITS analysis.

The interactions used in the study were threaded discussions
from three semesters of a USC undergraduate course in
computer science, including 2214 messages in 640 threads,
all discussing class material and posing questions about
problems.

The goal was to extract from the conversation the best
answer to the questions discussed. And, according to the
paper, the system works -- not perfectly, but much better
than one that selects answers at random. Random selection
got the answer (as determined by human inspection) right
87 out of 314 times, where the best implementation of the
HITS system was correct 221 times.

Speech act analysis classifies the statements in the record
according to what they do in the context of the discussion,
assigning each to one of thirteen kinds of acts, grouped in
three categories: inform, request, social-interaction.

The "inform" speech act category includes corrections,
descriptions, elaborations, suggestions, and answers to
questions, both simple and complex. "Requests" include not
just requests for information but also for action, namely
commands. "Social" speech acts include acknowledgements,
thanks, compliments, criticisms, objections, and supportive
statements.

Lexical analysis looks for similarities in the vocabulary of
responses to see which are related to each other. From this
the system can determine the threads of the conversation,
and decide when new subtopics are split off.

Finally, poster trustworthiness measures the degree to which
participants accept statements made by each individual.
This is determined by scoring responses to a given person's
posts as either negative or positive. Over time, people
whose statements are more positively viewed become more
central and more trusted in the online community.

To test the method, part of the data (the classification of the
speech acts) was initially human coded. After it was trained,
the machine system was then applied to the same data, and
its performance was compared to that of the human coder.
It achieved accuracy of between 65% and 70% - a figure
that is likely to improve.

How soon will it be possible to download a version that can
score a given poster's influence in his/her chat community?
"This technology has considerable potential for
commercialization," said Hovy.

Besides Hovy, the other members of the conversation study
include ISI computer scientists Erin Shaw and Jihie Kim as
well as graduate student Donghui Feng.